Abstract

The performance of the cooling tower is profoundly influenced by the climatic conditions. This study examines cooling tower performance in 42 diverse Saudi Arabian cities, addressing gaps in existing literature considering the performance parameters of temperature range, and cooling capacity. To assess the performance of the cooling tower, a mathematical model is developed and solved using the Engineering Equation Solver (EES). The assessment of the cooling tower’s performance is conducted with a focus on yearly and monthly average dry-bulb temperatures as well as relative humidity. Based on the yearly average weather data, the cooling tower in Jazan exhibits the lowest performance with a temperature range of 4°C and a 50-kW cooling capacity. In Abha, the cooling tower demonstrates the best performance despite different weather conditions with almost a 7.61°C range and 95.4-kW cooling capacity. Evaluating the monthly average weather data, the cooling tower in Abha excels compared to the cooling tower in the other cities, achieving a cooling capacity of 86.1-kW in August outperforming its counterpart in Jazan by 53.5%. The summer weather conditions in Abha have led to a slight reduction in the cooling capacity of only 16.3% in comparison to the cooling capacity in winter (January).

Abstract

Methane dry reforming is a promising method of converting CH4 and CO2 into valuable fuels. Conventional approaches require the combustion of methane to achieve high reaction temperatures, resulting in energy wastage. This study investigates the use of concentrated solar energy as an alternative driving source for methane dry reforming to achieve the dual goals of increasing fuel production and saving energy. To this end, we design and optimize a solar reactor featuring the synthesis, molding, and characterization of Ni/CeO2 catalysts. The performance of the methane dry reforming reaction is evaluated under different flow rates. The experimental results demonstrate that solar energy can be converted into fuel steadily for 8 hours with a solar-to-fuel efficiency of 13.98% when the catalyst is modeled to particle shape. This research makes a significant contribution to the development of eco-friendly and sustainable technologies for fuel production, offers application perspectives for a variety of industrial processes, and facilitates the integration of renewable energy sources.

Abstract

In the later stage of cycle steam stimulation(CSS), the production capacity decreases and the water cut increases. As an alternative development method to cycle steam stimulation, the flue gas-assisted cycle steam stimulation(FGACSS) has been widely applied in heavy oil development. However, existing research has not been able to reveal the mechanisms of different components in different zones, making it difficult to provide effective guidance. In this paper, based on the analysis of production dynamics from typical wells in the Moltuk field, a mechanistic model of FGACSS is constructed. The different zones of action for different components are delineated, and microscopic displacement experiments are conducted to study the flow states of oil, gas, and water in different zones at the microscopic level. The research results show that, based on the distribution of different components and reservoir parameters, the wellbore to far-well region can be divided into three zones: the high-temperature zone (0-25m), recombination zone (25-100m), and increased pressure zone (100-200m). In the high-temperature zone, the main mechanism is the viscosity reduction through condensation of steam. The flow state is water-oil, with different flow characteristics in large and small pores. In the recombination zone, viscosity reduction is achieved through heat conduction and CO2 dissolution. The presence of CO2 and N2 in the gas phase increases the pressure, and the flow state at the microscopic level is water-oil-gas-oil. The hot water and non-condensable gas displace different types of residual oil. In the increased pressure zone, only N2 is present, and the pressure continuously decreases. The flow state is gas-oil. Based on the dynamic analysis of typical wells, this paper proposes a multi-composition zoning coupling mechanism, providing reference for further research on FGACSS mechanisms and adjustments to production measures.

Abstract

Low-carbon mode is an important way to face global warming and extreme climate problems, and countries around the world have invested huge amounts of money in exploring perfect low-carbon energy-saving systems, and hydrogen energy, as the cleanest energy source, has been applied in this process. This paper proposes an optimal configuration model of electric-hydrogen hybrid energy storage system considering carbon trading and wind power fluctuation smoothing. Firstly, the basic principle of carbon trading is expressed, and on the basis of which a carbon trading stepwise cost model is proposed; then, the initial wind power signal is decomposed into the power generation demand portion and the hydrogen production portion; on the carbon trading cost model and the decomposition of the wind power signal, the hybrid electric-hydrogen system is constructed. On the basis of the carbon trading cost model and wind power signal decomposition, the hybrid electric-hydrogen system is constructed, and the optimization strategy is proposed under the objectives of maximizing wind energy consumption and optimizing the economy of hydrogen energy utilization. The study shows that under the stepped carbon trading model, the maximum utilization of clean energy can be obtained by smoothing the fluctuation of wind power, and the hydrogen produced can be sold to obtain economic benefits for reducing the cost of hydrogen production equipment.

Abstract

Ni-rich LiNixMnyCo1−x−yO2 (x ≥ 0.8) cathode is widely applied in high-end vehicle for its relatively high specific capacity and operating potential, which receives extensive attention in academia and industry. However, poor cycling performance and high cost hinder its further applications in lithium-ion batteries. In this work, 2-Methylenesuccinic anhydride is utilized as a functional cathode additive. Through the interaction between low valence Ni ions and anhydride, the facile approach allows functional 2-Methylenesuccinic anhydride more precisely work at the interface. As the operation potential rises, 2-Methylenesuccinic anhydride is oxidized prior to the electrolyte and a robust CEI layer rich in carbon oxygen double bond is built in the surface of cathode particles upon cycling, which has been analyzed by TEM and XPS. Besides, 2-Methylenesuccinic anhydride can absorb the trace of water in the cathode and electrolyte. As a result, the 0.5wt% 2-Methylenesuccinic anhydride modified NCM sample retains 91.6 % of initial capacity after charge-discharge 400 cycles at 2C. The novel modification strategy by adding a cathode additive is of important significance for mitigating deleterious side reactions in the interface for Ni-rich NCM materials.

Abstract

Battery management system (BMS) is crucial to ensure the efficiency and safety of the Lithium-ion battery pack by monitoring the real-time voltage, current, and temperature. In particular, the monitoring and warning system of the BMS is especially vital and demanding. However, most researchers separate the battery states as individual tasks and discuss them respectively, while state of charge (SOC), state of health (SOH), and state of temperature (SOT) are coupled and should be estimated simultaneously in one model. To satisfy the demand for the online monitoring and warning system, this paper proposed an integrated estimation model based on temporal convolution network (TCN) and multi-task learning taking the mutuality of the SOC, SOH and SOT into consideration. Specifically, four assessing indexes were obtained by analyzing the relationship, tendency and characteristic of the temperature and capacity during the battery aging process. Then, a multi-timescale model was built combined with the conception of multi-task learning, namely the hierarchical temporal convolutional network (HTCN), and the temperature varying tendency is predicted along with the battery states as different output tasks. At last, the model was transferred to test datasets to validate the generality, accuracy and robustness. Results show that the mean average error (MAE) of the SOC, SOH and SOT estimation are 1.37%, 0.95% and 1.01%, respectively. This paper provided a novel, practical and reliable route for the comprehensive BMS construction.

Abstract

Free-piston linear generator has become one of the new forms of energy conversion devices in the future due to its compact structure, multi-fuel adaptability, and easy modular arrangement. Based on system dynamics, thermodynamics and electromagnetism, this paper proposes the whole working process control structure of the free-piston linear generator, including the top control/monitor layer, the process control strategy layer and bottom actuator control layer, and carried out the control strategy of the system starting process, regulating process and stable generating process. In order to achieve the starting requirements, a muti-objective global optimization strategy based on neural network is established. Aiming at the problem of misfire during stable operation, a fast online identification and treatment based on motion characteristics-cylinder pressure is proposed, and the generator control strategy is changed to enable the system to quickly stabilize. In view of the above strategies, the simulation platform of the system is proposed, to verify the feasibility of each process control strategy of the system.

Abstract

In this paper, a systematic technology is developed to study the influence of pore-throat structure and fracture development on the water invasion behavior of fractured gas reservoirs, quantitatively characterize the law of water invasion, and then formulate a reasonable working system. In the experiment, nuclear magnetic resonance ( NMR ) and high-pressure mercury injection ( HPMI ) were used to characterize the pore structure of the core samples, and the distribution of the invaded water phase in the pore throat was clarified. The effect of water phase distribution on gas phase permeability was obtained by displacement test. Based on the existing capillary bundle model, three types of gas-water microscopic occurrence and seepage modes are divided from the microscopic pore-throat structure. Furthermore, considering the high conductivity of fractures in the reservoir, a mathematical model was formulated and generalized to characterize the seepage characteristics of fractured gas reservoirs. In this way, the effects of pore-throat structure and fracture development degree on water invasion law are comprehensively examined and analyzed. Subsequently, the model was promoted and applied to the dynamic prediction and scheme formulation of a fractured gas reservoirs. The research shows that during the flow of edge-aquifer along the fracture, it will invade the matrix under the action of large capillary force and reserve in a large number of small pore throats, thus forming a large amount of closed residual gas. which greatly restricts the gas recovery rate and recovery rate of the reservoirs. The simulation shows that the production pressure difference in the study area should be controlled within 5 MPa, and the early strong drainage measures at the edge should be used to assist in increasing production. Reasonable water control measures can improve the recovery rate of gas reservoirs by more than 15 %.

Abstract

The petroleum industry is one of the fastest-growing sectors and has made a significant contribution to the economic growth of developing countries. Wastewater generated by the petroleum industry contains a variety of organic pollutants. These organic compounds exist in highly complex forms in discharged water and cause environmental hazards. Sono-electrochemical system is emerging as a future trend due to its clean and non-secondary pollution characteristics. This process combines ultrasound and electrochemical methods to enhance the reaction rate constants for pollutant degradation. However, in the system design process, the complex interactions between EC, US, pollutants, and environmental parameters significantly impact the outcomes. Therefore, predicting the kinetic constants of organic compound degradation in US-EC systems within complex reaction systems is challenging. In this study, Machine learning models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost) were employed to predict the degradation rates of organic compounds in US-EC systems. Comparative analysis of the prediction results from different models showed that XGBoost performed exceptionally well, with R2 and RMSE values of 0.97 and 0.0006, respectively. SHAP analysis was conducted to evaluate the impact of design parameters on the model’s predictive performance, and the results indicated that ultrasonic frequency, ultrasonic power, and the distance ‘r’ from the ultrasonic transducer to the electrode had the most significant influence on the model’s predictive performance. This method effectively guides the parameter design of US-EC systems and enables accurate predictions of the degradation rates of organic compounds.

Abstract

This study determines the cost reducing effect of hydrogen supply-push targets, which will indicate the sufficiency of current UK government policy to initiate a hydrogen economy within the industrial sector. This study will also answer the question “What demand-pull policies can support fuel switching to hydrogen in UK industry?” A novel mixed-methods approach is used, in-depth rapid evidence assessment and a macro market penetration assessment to understand how to best establish an industrial hydrogen economy. Our findings show that without demand-pull policies, 65 GW to 350 GW of hydrogen supply is required to achieve price parity with natural gas.